Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f124715e470>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f1240b20f60>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """

    input_images = tf.placeholder( tf.float32, shape=(None, image_width, image_height, image_channels) )
    input_z = tf.placeholder( tf.float32, shape=(None, z_dim))
    lr = tf.placeholder( tf.float32 )    
    
    return input_images, input_z, lr


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [71]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """  

    with tf.variable_scope('discriminator', reuse=reuse):
       alpha = 0.2
       # Input layer is 28x28x3
       x1 = tf.layers.conv2d(images, 64, 5, strides=1, padding='valid')
       relu1 = tf.maximum(alpha * x1, x1)
       # 24x24x32
                
       x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
       bn2 = tf.layers.batch_normalization(x2, training=True)
       relu2 = tf.maximum(alpha * bn2, bn2)
       # 12x12x64
           
       x3 = tf.layers.conv2d(relu1, 256, 5, strides=3, padding='same')
       bn3 = tf.layers.batch_normalization(x3, training=True)
       relu3 = tf.maximum(alpha * bn3, bn3)
       # 4x4x256
    
       # Flatten it
       flat = tf.reshape(relu3, (-1, 4*4*256))
       logits = tf.layers.dense(flat, 1)
       out = tf.sigmoid(logits)
       
       return out, logits
    


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [72]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    alpha = 0.2
    with tf.variable_scope('generator', reuse=(not is_train)):
        # First fully connected layer
        x1 = tf.layers.dense(z, 4*4*256)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 4, 4, 256))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 4x4x256
              
        x2 = tf.layers.conv2d_transpose(x1, 128, 5, strides=3, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 12x12x64
        
        x3 = tf.layers.conv2d_transpose(x2, 64, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        # 24x24x32
               
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=1, padding='valid')
        # 28x28x3 now
               
        out = tf.tanh(logits)
        
        return out

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [73]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """

    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [74]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [75]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [76]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    tf.reset_default_graph()
        
    input_images, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_images, input_z, data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    steps = 0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1

                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_images: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_images: batch_images, input_z: batch_z, lr: learning_rate})

                if steps % 100 == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_images: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)               
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [78]:
batch_size = 32
z_dim = 10
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.0274... Generator Loss: 4.8357
Epoch 1/2... Discriminator Loss: 0.2188... Generator Loss: 2.2453
Epoch 1/2... Discriminator Loss: 1.1651... Generator Loss: 4.3773
Epoch 1/2... Discriminator Loss: 0.2930... Generator Loss: 4.0026
Epoch 1/2... Discriminator Loss: 0.4579... Generator Loss: 1.5222
Epoch 1/2... Discriminator Loss: 0.6888... Generator Loss: 1.6793
Epoch 1/2... Discriminator Loss: 1.6715... Generator Loss: 0.2919
Epoch 1/2... Discriminator Loss: 0.5115... Generator Loss: 2.4323
Epoch 1/2... Discriminator Loss: 1.1225... Generator Loss: 0.5572
Epoch 1/2... Discriminator Loss: 0.5089... Generator Loss: 1.4544
Epoch 1/2... Discriminator Loss: 0.5855... Generator Loss: 2.1034
Epoch 1/2... Discriminator Loss: 0.8441... Generator Loss: 0.7938
Epoch 1/2... Discriminator Loss: 0.7739... Generator Loss: 1.4231
Epoch 1/2... Discriminator Loss: 0.5772... Generator Loss: 1.3816
Epoch 1/2... Discriminator Loss: 0.5253... Generator Loss: 1.5149
Epoch 1/2... Discriminator Loss: 0.5626... Generator Loss: 2.6694
Epoch 1/2... Discriminator Loss: 0.5350... Generator Loss: 1.2592
Epoch 1/2... Discriminator Loss: 0.6606... Generator Loss: 1.4079
Epoch 2/2... Discriminator Loss: 0.8989... Generator Loss: 1.4026
Epoch 2/2... Discriminator Loss: 0.6393... Generator Loss: 1.1045
Epoch 2/2... Discriminator Loss: 0.6371... Generator Loss: 1.4082
Epoch 2/2... Discriminator Loss: 1.2782... Generator Loss: 0.6100
Epoch 2/2... Discriminator Loss: 0.4873... Generator Loss: 1.4904
Epoch 2/2... Discriminator Loss: 0.7708... Generator Loss: 1.8201
Epoch 2/2... Discriminator Loss: 0.6585... Generator Loss: 1.7634
Epoch 2/2... Discriminator Loss: 0.7601... Generator Loss: 0.9136
Epoch 2/2... Discriminator Loss: 0.7007... Generator Loss: 1.1269
Epoch 2/2... Discriminator Loss: 0.6004... Generator Loss: 1.6067
Epoch 2/2... Discriminator Loss: 0.6401... Generator Loss: 1.2317
Epoch 2/2... Discriminator Loss: 1.0099... Generator Loss: 0.8367
Epoch 2/2... Discriminator Loss: 0.8126... Generator Loss: 2.1453
Epoch 2/2... Discriminator Loss: 1.1855... Generator Loss: 2.2563
Epoch 2/2... Discriminator Loss: 0.6949... Generator Loss: 1.5305
Epoch 2/2... Discriminator Loss: 1.2445... Generator Loss: 0.5223
Epoch 2/2... Discriminator Loss: 0.6393... Generator Loss: 2.2181
Epoch 2/2... Discriminator Loss: 1.2058... Generator Loss: 4.6117
Epoch 2/2... Discriminator Loss: 1.4593... Generator Loss: 0.4493
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-78-ccf1f8354a9b> in <module>()
     13 with tf.Graph().as_default():
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
---> 15           mnist_dataset.shape, mnist_dataset.image_mode)

/home/dj/anaconda3/envs/dl_env_linux_gpu/lib/python3.5/contextlib.py in __exit__(self, type, value, traceback)
     64         if type is None:
     65             try:
---> 66                 next(self.gen)
     67             except StopIteration:
     68                 return

/home/dj/anaconda3/envs/dl_env_linux_gpu/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in get_controller(self, default)
   3626     finally:
   3627       if self._enforce_nesting:
-> 3628         if self.stack[-1] is not default:
   3629           raise AssertionError(
   3630               "Nesting violated for default stack of %s objects"

IndexError: list index out of range

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [79]:
batch_size = 32
z_dim = 50
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 0.0314... Generator Loss: 4.1779
Epoch 1/1... Discriminator Loss: 0.1074... Generator Loss: 2.9691
Epoch 1/1... Discriminator Loss: 0.4718... Generator Loss: 1.9403
Epoch 1/1... Discriminator Loss: 1.6924... Generator Loss: 0.5471
Epoch 1/1... Discriminator Loss: 0.7475... Generator Loss: 1.2906
Epoch 1/1... Discriminator Loss: 0.2615... Generator Loss: 2.4416
Epoch 1/1... Discriminator Loss: 1.1479... Generator Loss: 1.3464
Epoch 1/1... Discriminator Loss: 0.2786... Generator Loss: 11.6694
Epoch 1/1... Discriminator Loss: 1.2467... Generator Loss: 0.6835
Epoch 1/1... Discriminator Loss: 0.9603... Generator Loss: 1.0201
Epoch 1/1... Discriminator Loss: 0.8025... Generator Loss: 2.0753
Epoch 1/1... Discriminator Loss: 0.6656... Generator Loss: 1.3302
Epoch 1/1... Discriminator Loss: 1.5283... Generator Loss: 0.4049
Epoch 1/1... Discriminator Loss: 0.7323... Generator Loss: 1.0271
Epoch 1/1... Discriminator Loss: 1.4946... Generator Loss: 0.4792
Epoch 1/1... Discriminator Loss: 0.5376... Generator Loss: 2.7445
Epoch 1/1... Discriminator Loss: 1.0910... Generator Loss: 0.7156
Epoch 1/1... Discriminator Loss: 0.9458... Generator Loss: 1.4482
Epoch 1/1... Discriminator Loss: 0.9714... Generator Loss: 1.0181
Epoch 1/1... Discriminator Loss: 0.8787... Generator Loss: 1.3635
Epoch 1/1... Discriminator Loss: 0.9733... Generator Loss: 1.3977
Epoch 1/1... Discriminator Loss: 1.1609... Generator Loss: 0.8552
Epoch 1/1... Discriminator Loss: 0.8284... Generator Loss: 1.1117
Epoch 1/1... Discriminator Loss: 1.3327... Generator Loss: 0.5207
Epoch 1/1... Discriminator Loss: 1.1514... Generator Loss: 1.0354
Epoch 1/1... Discriminator Loss: 1.0507... Generator Loss: 0.9761
Epoch 1/1... Discriminator Loss: 0.9375... Generator Loss: 1.2776
Epoch 1/1... Discriminator Loss: 1.1079... Generator Loss: 1.1105
Epoch 1/1... Discriminator Loss: 1.0008... Generator Loss: 1.1074
Epoch 1/1... Discriminator Loss: 1.0666... Generator Loss: 1.1902
Epoch 1/1... Discriminator Loss: 1.0961... Generator Loss: 0.8002
Epoch 1/1... Discriminator Loss: 1.2472... Generator Loss: 0.7904
Epoch 1/1... Discriminator Loss: 1.1379... Generator Loss: 1.0543
Epoch 1/1... Discriminator Loss: 1.2500... Generator Loss: 0.9862
Epoch 1/1... Discriminator Loss: 1.3518... Generator Loss: 0.8696
Epoch 1/1... Discriminator Loss: 1.3679... Generator Loss: 0.9344
Epoch 1/1... Discriminator Loss: 1.1328... Generator Loss: 0.7300
Epoch 1/1... Discriminator Loss: 1.2855... Generator Loss: 0.7026
Epoch 1/1... Discriminator Loss: 1.1514... Generator Loss: 0.9196
Epoch 1/1... Discriminator Loss: 1.3480... Generator Loss: 0.6998
Epoch 1/1... Discriminator Loss: 1.1461... Generator Loss: 0.8465
Epoch 1/1... Discriminator Loss: 1.3428... Generator Loss: 0.6294
Epoch 1/1... Discriminator Loss: 1.3065... Generator Loss: 0.7159
Epoch 1/1... Discriminator Loss: 1.2450... Generator Loss: 0.6912
Epoch 1/1... Discriminator Loss: 1.3226... Generator Loss: 0.8053
Epoch 1/1... Discriminator Loss: 1.3559... Generator Loss: 0.9353
Epoch 1/1... Discriminator Loss: 1.1302... Generator Loss: 1.0995
Epoch 1/1... Discriminator Loss: 1.2655... Generator Loss: 0.7511
Epoch 1/1... Discriminator Loss: 1.2351... Generator Loss: 0.8741
Epoch 1/1... Discriminator Loss: 1.3095... Generator Loss: 0.6691
Epoch 1/1... Discriminator Loss: 1.2127... Generator Loss: 0.9369
Epoch 1/1... Discriminator Loss: 1.2833... Generator Loss: 0.7265
Epoch 1/1... Discriminator Loss: 1.3105... Generator Loss: 0.7212
Epoch 1/1... Discriminator Loss: 1.3810... Generator Loss: 0.7120
Epoch 1/1... Discriminator Loss: 1.2331... Generator Loss: 0.6832
Epoch 1/1... Discriminator Loss: 1.4058... Generator Loss: 0.8910
Epoch 1/1... Discriminator Loss: 1.3192... Generator Loss: 0.5580
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
/home/dj/anaconda3/envs/dl_env_linux_gpu/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in get_controller(self, default)
   3624       self.stack.append(default)
-> 3625       yield default
   3626     finally:

<ipython-input-79-c6f2c9f7feb4> in <module>()
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
---> 15           celeba_dataset.shape, celeba_dataset.image_mode)

<ipython-input-76-225210dd7033> in train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode)
     41 
---> 42                     show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
     43 

<ipython-input-75-73239a44c0dc> in show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode)
     24     pyplot.imshow(images_grid, cmap=cmap)
---> 25     pyplot.show()

/home/dj/anaconda3/envs/dl_env_linux_gpu/lib/python3.5/site-packages/matplotlib/pyplot.py in show(*args, **kw)
    252     global _show
--> 253     return _show(*args, **kw)
    254 

/home/dj/anaconda3/envs/dl_env_linux_gpu/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py in show(close, block)
     35         for figure_manager in Gcf.get_all_fig_managers():
---> 36             display(figure_manager.canvas.figure)
     37     finally:

/home/dj/anaconda3/envs/dl_env_linux_gpu/lib/python3.5/site-packages/IPython/core/display.py in display(*objs, **kwargs)
    163         else:
--> 164             format_dict, md_dict = format(obj, include=include, exclude=exclude)
    165             if not format_dict:

/home/dj/anaconda3/envs/dl_env_linux_gpu/lib/python3.5/site-packages/IPython/core/formatters.py in format(self, obj, include, exclude)
    144             try:
--> 145                 data = formatter(obj)
    146             except:

<decorator-gen-9> in __call__(self, obj)

/home/dj/anaconda3/envs/dl_env_linux_gpu/lib/python3.5/site-packages/IPython/core/formatters.py in catch_format_error(method, self, *args, **kwargs)
    189     try:
--> 190         r = method(self, *args, **kwargs)
    191     except NotImplementedError:

/home/dj/anaconda3/envs/dl_env_linux_gpu/lib/python3.5/site-packages/IPython/core/formatters.py in __call__(self, obj)
    306             else:
--> 307                 return printer(obj)
    308             # Finally look for special method names

/home/dj/anaconda3/envs/dl_env_linux_gpu/lib/python3.5/site-packages/IPython/core/pylabtools.py in <lambda>(fig)
    239     if 'png' in formats:
--> 240         png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
    241     if 'retina' in formats or 'png2x' in formats:

/home/dj/anaconda3/envs/dl_env_linux_gpu/lib/python3.5/site-packages/IPython/core/pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs)
    123     bytes_io = BytesIO()
--> 124     fig.canvas.print_figure(bytes_io, **kw)
    125     data = bytes_io.getvalue()

/home/dj/anaconda3/envs/dl_env_linux_gpu/lib/python3.5/site-packages/matplotlib/backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)
   2191                     dryrun=True,
-> 2192                     **kwargs)
   2193                 renderer = self.figure._cachedRenderer

/home/dj/anaconda3/envs/dl_env_linux_gpu/lib/python3.5/site-packages/matplotlib/backends/backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs)
    555         try:
--> 556             _png.write_png(renderer._renderer, filename_or_obj, self.figure.dpi)
    557         finally:

KeyboardInterrupt: 

During handling of the above exception, another exception occurred:

IndexError                                Traceback (most recent call last)
<ipython-input-79-c6f2c9f7feb4> in <module>()
     13 with tf.Graph().as_default():
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
---> 15           celeba_dataset.shape, celeba_dataset.image_mode)

/home/dj/anaconda3/envs/dl_env_linux_gpu/lib/python3.5/contextlib.py in __exit__(self, type, value, traceback)
     75                 value = type()
     76             try:
---> 77                 self.gen.throw(type, value, traceback)
     78                 raise RuntimeError("generator didn't stop after throw()")
     79             except StopIteration as exc:

/home/dj/anaconda3/envs/dl_env_linux_gpu/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in get_controller(self, default)
   3626     finally:
   3627       if self._enforce_nesting:
-> 3628         if self.stack[-1] is not default:
   3629           raise AssertionError(
   3630               "Nesting violated for default stack of %s objects"

IndexError: list index out of range

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.